Development and validation of a model that predicts the risk of diabetic retinopathy in type 2 diabetes mellitus patients.

Aims: Diabetic retinopathy is the leading cause of blindness in people with type 2 diabetes. To enable primary care physicians to identify high-risk type 2 diabetic patients with diabetic retinopathy at an early stage, we developed a nomogram model to predict the risk of developing diabetic retinopathy in the Xinjiang type 2 diabetic population. Methods: In a retrospective study, we collected data on 834 patients with type 2 diabetes through an electronic medical record system. Stepwise regression was used to filter variables. Logistic regression was applied to build a nomogram prediction model and further validated in the training set. The c-index, forest plot, calibration plot, and clinical decision curve analysis were used to comprehensively validate the model and evaluate its accuracy and clinical validity. Results: Four predictors were selected to establish the final model: hypertension, blood urea nitrogen, duration of diabetes, and diabetic peripheral neuropathy. The model displayed medium predictive power with a C-index of 0.781(95%CI:0.741–0.822) in the training set and 0.865(95%CI:0.807–0.923)in the validation set. The calibration curve of the DR probability shows that the predicted results of the nomogram are in good agreement with the actual results. Decision curve analysis demonstrated that the novel nomogram was clinically valuable. Conclusions: The nomogram of the risk of developing diabetic nephropathy contains 4 characteristics. that can help primary care physicians quickly identify individuals at high risk of developing DR in patients with type 2 diabetes, to intervene as soon as possible. [ABSTRACT FROM AUTHOR]

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